Assessment of Educational Neuromyths among Teachers and Teacher Candidates
Bibliographic record
Abstract
The aim of study is to determine the neuromyth level of teachers and pre-teachers and reveal if there is significant difference in terms of some variables (gender, class, etc.). Research was designed in survey model. The research sample was formed with 241 teachers and 511 teacher candidates. In the collection of data, “Educational neuromyhts test” that has 31 questions with options “right, wrong, I have no idea” that was created by the authors by applying reliability studies. Score that can be taken from measuring tool are in the range of 0-31. According to the findings; while teachers are having an average score of “18,87”, teacher candidates received an average score of “16,70”. According to this result, teacher and teacher candidates have misplaced half of the questions of neurometry. While comparing the scores of teachers and teacher candidates, a significant difference in favor of the teachers (p=.000) were found. The results of the research are expected to led to a debate on “brain and learning” issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".